arXiv subjects

Carlo Alessi

Publications and source records attributed to Carlo Alessi.

4 recordsLinked to original sources

arXiv2025

Zero-shot Whole-Body Manipulation with a Large-Scale Soft Robotic Torso via Guided Reinforcement Learning

Whole-body manipulation is a powerful yet underexplored approach that enables robots to interact with large, heavy, or awkward objects using more than just their end-effectors. Soft robots, with their inherent passive compliance, are particularly well-suited for such contact-rich manipulation tasks, but their uncertainties in kinematics and dynamics pose significant challenges for simulation and control. In this work, we address this challenge with a simulation that can run up to 350x real time on a single thread in MuJoCo and provide a detailed analysis of the critical tradeoffs between speed and accuracy for this simulation. Using this framework, we demonstrate a successful zero-shot sim-to-real transfer of a learned whole-body manipulation policy, achieving an 88% success rate on the Baloo hardware platform. We show that guiding RL with a simple motion primitive is critical to this success where standard reward shaping methods struggled to produce a stable and successful policy for whole-body manipulation. Furthermore, our analysis reveals that the learned policy does not simply mimic the motion primitive. It exhibits beneficial reactive behavior, such as re-grasping and perturbation recovery. We analyze and contrast this learned policy against an open-loop baseline to show that the policy can also exhibit aggressive over-corrections under perturbation. To our knowledge, this is the first demonstration of forceful, six-DoF whole-body manipulation using two continuum soft arms on a large-scale platform (10 kg payloads), with zero-shot policy transfer.

cs.RO↗

arXiv2025

Domain Translation of a Soft Robotic Arm using Conditional Cycle Generative Adversarial Network

Deep learning provides a powerful method for modeling the dynamics of soft robots, offering advantages over traditional analytical approaches that require precise knowledge of the robot's structure, material properties, and other physical characteristics. Given the inherent complexity and non-linearity of these systems, extracting such details can be challenging. The mappings learned in one domain cannot be directly transferred to another domain with different physical properties. This challenge is particularly relevant for soft robots, as their materials gradually degrade over time. In this paper, we introduce a domain translation framework based on a conditional cycle generative adversarial network (CCGAN) to enable knowledge transfer from a source domain to a target domain. Specifically, we employ a dynamic learning approach to adapt a pose controller trained in a standard simulation environment to a domain with tenfold increased viscosity. Our model learns from input pressure signals conditioned on corresponding end-effector positions and orientations in both domains. We evaluate our approach through trajectory-tracking experiments across five distinct shapes and further assess its robustness under noise perturbations and periodicity tests. The results demonstrate that CCGAN-GP effectively facilitates cross-domain skill transfer, paving the way for more adaptable and generalizable soft robotic controllers.

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arXiv2025

HannesImitation: Grasping with the Hannes Prosthetic Hand via Imitation Learning

Recent advancements in control of prosthetic hands have focused on increasing autonomy through the use of cameras and other sensory inputs. These systems aim to reduce the cognitive load on the user by automatically controlling certain degrees of freedom. In robotics, imitation learning has emerged as a promising approach for learning grasping and complex manipulation tasks while simplifying data collection. Its application to the control of prosthetic hands remains, however, largely unexplored. Bridging this gap could enhance dexterity restoration and enable prosthetic devices to operate in more unconstrained scenarios, where tasks are learned from demonstrations rather than relying on manually annotated sequences. To this end, we present HannesImitationPolicy, an imitation learning-based method to control the Hannes prosthetic hand, enabling object grasping in unstructured environments. Moreover, we introduce the HannesImitationDataset comprising grasping demonstrations in table, shelf, and human-to-prosthesis handover scenarios. We leverage such data to train a single diffusion policy and deploy it on the prosthetic hand to predict the wrist orientation and hand closure for grasping. Experimental evaluation demonstrates successful grasps across diverse objects and conditions. Finally, we show that the policy outperforms a segmentation-based visual servo controller in unstructured scenarios. Additional material is provided on our project page: https://hsp-iit.github.io/HannesImitation

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arXiv2024

Rod models in continuum and soft robot control: a review

Continuum and soft robots can transform automation tasks requiring compliant interaction in constrained or unstructured environments, including healthcare, agriculture, marine, and space applications. However, their complex mechanics introduce significant challenges in modeling and control. Low-dimensional continuum mechanical models, such as rod theories, effectively capture the large deformations of slender bodies in contact-rich scenarios while balancing accuracy and computational efficiency. This paper presents a vertical survey of rod models for continuum and soft robots, spanning their mathematical foundations, robot modeling, and control applications. We review the main rod theories adopted in soft robotics and introduce a deformation-based classification of rod models for continuum and soft robots. Furthermore, we survey recent model-based and learning-based control strategies leveraging rod models, highlighting their role in manipulation and physical interaction tasks. Finally, we discuss advantages, limitations, research gaps, and emerging directions of rod-based approaches. This paper aims to serve as a reference for developing models and control strategies for continuum and soft robots.

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